{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 时间序列的演示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "now = datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2018, 9, 21, 6, 15, 51, 115688)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(0, 10)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta = datetime(2017,1,18,11,1,20,110) - datetime(2017,1,18,11,1,10,110)\n",
    "delta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**如何获取timedelta的数据，日和秒，要复数**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta.seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "stamp = datetime(2018,11,11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**如何从时间格式变成String格式**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2018-11-11 00:00:00'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(stamp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**周的表示**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'45'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp.strftime('%U')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**万能时间识别**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2018, 1, 1, 10, 10, 1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dateutil.parser import parse\n",
    "parse('2018/01/01 10:10:01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-01', '2017-01-02'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetrs = ['2018/01/01','2017/01/02']\n",
    "datetrs_2datetime = pd.to_datetime(datetrs)\n",
    "datetrs_2datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "ts = pd.Series(np.random.randn(2),index = datetrs_2datetime)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**如何变成时间序列的dataframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-01', '2017-01-02'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**直接输入日期也可以找到对应的值**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-01   -0.401706\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts['2018/01/01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-01   -0.401706\n",
       "2017-01-02   -1.755889\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**长时间序列的例子**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "longer_ts = pd.Series(np.random.randn(1000),index=pd.date_range('1/1/2011',periods=1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-01    1.115561\n",
       "2011-01-02   -2.354566\n",
       "2011-01-03    1.315744\n",
       "2011-01-04    0.028046\n",
       "2011-01-05    0.040957\n",
       "2011-01-06   -0.834180\n",
       "2011-01-07   -0.977188\n",
       "2011-01-08    0.715274\n",
       "2011-01-09   -1.263560\n",
       "2011-01-10   -0.137901\n",
       "2011-01-11   -1.306840\n",
       "2011-01-12   -1.173332\n",
       "2011-01-13    0.704828\n",
       "2011-01-14    0.334531\n",
       "2011-01-15   -0.672806\n",
       "2011-01-16   -1.157035\n",
       "2011-01-17    0.875492\n",
       "2011-01-18   -0.621911\n",
       "2011-01-19    0.750415\n",
       "2011-01-20   -0.570674\n",
       "2011-01-21   -0.233889\n",
       "2011-01-22   -0.461361\n",
       "2011-01-23    1.946082\n",
       "2011-01-24    1.914943\n",
       "2011-01-25   -0.185635\n",
       "2011-01-26   -1.922463\n",
       "2011-01-27   -0.965326\n",
       "2011-01-28   -0.634364\n",
       "2011-01-29   -0.114259\n",
       "2011-01-30    0.508825\n",
       "                ...   \n",
       "2013-08-28    0.644517\n",
       "2013-08-29    0.272357\n",
       "2013-08-30   -1.264444\n",
       "2013-08-31   -1.142795\n",
       "2013-09-01   -0.121534\n",
       "2013-09-02    1.327212\n",
       "2013-09-03   -0.338651\n",
       "2013-09-04   -0.258968\n",
       "2013-09-05    0.670993\n",
       "2013-09-06   -1.042289\n",
       "2013-09-07   -1.322000\n",
       "2013-09-08    1.278827\n",
       "2013-09-09    0.638996\n",
       "2013-09-10   -0.848484\n",
       "2013-09-11    1.043447\n",
       "2013-09-12    1.993450\n",
       "2013-09-13    0.823689\n",
       "2013-09-14    0.961072\n",
       "2013-09-15    0.340609\n",
       "2013-09-16    0.993091\n",
       "2013-09-17   -0.446388\n",
       "2013-09-18    2.373558\n",
       "2013-09-19   -1.001969\n",
       "2013-09-20    0.276383\n",
       "2013-09-21    0.152008\n",
       "2013-09-22   -0.250206\n",
       "2013-09-23   -0.221350\n",
       "2013-09-24    1.407906\n",
       "2013-09-25   -0.570860\n",
       "2013-09-26    0.286521\n",
       "Freq: D, Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longer_ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**时间切片**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01    0.117587\n",
       "2013-01-02    0.226600\n",
       "2013-01-03    1.506159\n",
       "2013-01-04    1.336594\n",
       "2013-01-05    0.387199\n",
       "2013-01-06   -0.333999\n",
       "2013-01-07   -0.960374\n",
       "2013-01-08    1.353667\n",
       "2013-01-09   -0.374041\n",
       "2013-01-10    1.012735\n",
       "2013-01-11   -0.286643\n",
       "2013-01-12   -1.665110\n",
       "2013-01-13    1.542941\n",
       "2013-01-14    0.385298\n",
       "2013-01-15    0.848728\n",
       "2013-01-16    0.786104\n",
       "2013-01-17    1.378748\n",
       "2013-01-18    0.435149\n",
       "2013-01-19   -0.128094\n",
       "2013-01-20    0.634306\n",
       "2013-01-21   -1.177628\n",
       "2013-01-22    1.103957\n",
       "2013-01-23   -1.608871\n",
       "2013-01-24    1.203211\n",
       "2013-01-25   -1.181108\n",
       "2013-01-26   -1.472622\n",
       "2013-01-27    1.412668\n",
       "2013-01-28   -0.214824\n",
       "2013-01-29    0.603896\n",
       "2013-01-30   -0.011322\n",
       "                ...   \n",
       "2013-08-28    0.644517\n",
       "2013-08-29    0.272357\n",
       "2013-08-30   -1.264444\n",
       "2013-08-31   -1.142795\n",
       "2013-09-01   -0.121534\n",
       "2013-09-02    1.327212\n",
       "2013-09-03   -0.338651\n",
       "2013-09-04   -0.258968\n",
       "2013-09-05    0.670993\n",
       "2013-09-06   -1.042289\n",
       "2013-09-07   -1.322000\n",
       "2013-09-08    1.278827\n",
       "2013-09-09    0.638996\n",
       "2013-09-10   -0.848484\n",
       "2013-09-11    1.043447\n",
       "2013-09-12    1.993450\n",
       "2013-09-13    0.823689\n",
       "2013-09-14    0.961072\n",
       "2013-09-15    0.340609\n",
       "2013-09-16    0.993091\n",
       "2013-09-17   -0.446388\n",
       "2013-09-18    2.373558\n",
       "2013-09-19   -1.001969\n",
       "2013-09-20    0.276383\n",
       "2013-09-21    0.152008\n",
       "2013-09-22   -0.250206\n",
       "2013-09-23   -0.221350\n",
       "2013-09-24    1.407906\n",
       "2013-09-25   -0.570860\n",
       "2013-09-26    0.286521\n",
       "Freq: D, Length: 269, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longer_ts['2013/01/01':]  ##longer_ts[datetime(2013,1,1):] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01    0.117587\n",
       "2013-01-02    0.226600\n",
       "2013-01-03    1.506159\n",
       "2013-01-04    1.336594\n",
       "2013-01-05    0.387199\n",
       "2013-01-06   -0.333999\n",
       "2013-01-07   -0.960374\n",
       "2013-01-08    1.353667\n",
       "2013-01-09   -0.374041\n",
       "2013-01-10    1.012735\n",
       "2013-01-11   -0.286643\n",
       "2013-01-12   -1.665110\n",
       "2013-01-13    1.542941\n",
       "2013-01-14    0.385298\n",
       "2013-01-15    0.848728\n",
       "2013-01-16    0.786104\n",
       "2013-01-17    1.378748\n",
       "2013-01-18    0.435149\n",
       "2013-01-19   -0.128094\n",
       "2013-01-20    0.634306\n",
       "2013-01-21   -1.177628\n",
       "2013-01-22    1.103957\n",
       "2013-01-23   -1.608871\n",
       "2013-01-24    1.203211\n",
       "2013-01-25   -1.181108\n",
       "2013-01-26   -1.472622\n",
       "2013-01-27    1.412668\n",
       "2013-01-28   -0.214824\n",
       "2013-01-29    0.603896\n",
       "2013-01-30   -0.011322\n",
       "2013-01-31   -0.104901\n",
       "2013-02-01    2.200497\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longer_ts['2013/01/01':'2013/02/01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-01    1.115561\n",
       "2011-01-02   -2.354566\n",
       "2011-01-03    1.315744\n",
       "2011-01-04    0.028046\n",
       "2011-01-05    0.040957\n",
       "2011-01-06   -0.834180\n",
       "2011-01-07   -0.977188\n",
       "2011-01-08    0.715274\n",
       "2011-01-09   -1.263560\n",
       "2011-01-10   -0.137901\n",
       "2011-01-11   -1.306840\n",
       "2011-01-12   -1.173332\n",
       "2011-01-13    0.704828\n",
       "2011-01-14    0.334531\n",
       "2011-01-15   -0.672806\n",
       "2011-01-16   -1.157035\n",
       "2011-01-17    0.875492\n",
       "2011-01-18   -0.621911\n",
       "2011-01-19    0.750415\n",
       "2011-01-20   -0.570674\n",
       "2011-01-21   -0.233889\n",
       "2011-01-22   -0.461361\n",
       "2011-01-23    1.946082\n",
       "2011-01-24    1.914943\n",
       "2011-01-25   -0.185635\n",
       "2011-01-26   -1.922463\n",
       "2011-01-27   -0.965326\n",
       "2011-01-28   -0.634364\n",
       "2011-01-29   -0.114259\n",
       "2011-01-30    0.508825\n",
       "                ...   \n",
       "2012-12-03    0.025217\n",
       "2012-12-04   -0.383672\n",
       "2012-12-05    0.439629\n",
       "2012-12-06   -0.775169\n",
       "2012-12-07   -0.429217\n",
       "2012-12-08   -0.260058\n",
       "2012-12-09   -0.272054\n",
       "2012-12-10    1.023774\n",
       "2012-12-11   -1.079227\n",
       "2012-12-12    0.353465\n",
       "2012-12-13   -0.256901\n",
       "2012-12-14    2.183349\n",
       "2012-12-15    1.610908\n",
       "2012-12-16    0.661997\n",
       "2012-12-17   -0.184935\n",
       "2012-12-18    0.972859\n",
       "2012-12-19    1.286695\n",
       "2012-12-20    0.539342\n",
       "2012-12-21    1.560294\n",
       "2012-12-22    1.225465\n",
       "2012-12-23    0.221810\n",
       "2012-12-24    1.678055\n",
       "2012-12-25   -0.008235\n",
       "2012-12-26   -1.208244\n",
       "2012-12-27   -0.014805\n",
       "2012-12-28    0.767013\n",
       "2012-12-29   -0.555252\n",
       "2012-12-30   -0.014602\n",
       "2012-12-31   -0.155505\n",
       "2013-01-01    0.117587\n",
       "Freq: D, Length: 732, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longer_ts.truncate(after = '2013/01/01') ## 在日期以后就不要了"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**重复索引的处理办法**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017/01/01    0\n",
       "2017/01/02    1\n",
       "2017/01/03    2\n",
       "2017/01/02    3\n",
       "dtype: int32"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = ['2017/01/01','2017/01/02','2017/01/03','2017/01/02']\n",
    "dup_ts = pd.Series(np.arange(4),index = dates)\n",
    "dup_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_ts.index.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017/01/01    0\n",
       "2017/01/02    2\n",
       "2017/01/03    2\n",
       "dtype: int32"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = dup_ts.groupby(level=0) ## level=0，索引的唯一一层\n",
    "grouped.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017/01/01    0\n",
       "2017/01/02    4\n",
       "2017/01/03    2\n",
       "dtype: int32"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017/01/01    1\n",
       "2017/01/02    2\n",
       "2017/01/03    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-01   -0.401706\n",
       "2017-01-02   -1.755889\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**pd.date_range('4/1/2011','5/2/2011') or period生成日期范围**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-04-01', '2011-04-02', '2011-04-03', '2011-04-04',\n",
       "               '2011-04-05', '2011-04-06', '2011-04-07', '2011-04-08',\n",
       "               '2011-04-09', '2011-04-10', '2011-04-11', '2011-04-12',\n",
       "               '2011-04-13', '2011-04-14', '2011-04-15', '2011-04-16',\n",
       "               '2011-04-17', '2011-04-18', '2011-04-19', '2011-04-20',\n",
       "               '2011-04-21', '2011-04-22', '2011-04-23', '2011-04-24',\n",
       "               '2011-04-25', '2011-04-26', '2011-04-27', '2011-04-28',\n",
       "               '2011-04-29', '2011-04-30', '2011-05-01', '2011-05-02'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('4/1/2011','5/2/2011')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-04-01', '2011-04-02', '2011-04-03', '2011-04-04',\n",
       "               '2011-04-05', '2011-04-06', '2011-04-07', '2011-04-08',\n",
       "               '2011-04-09', '2011-04-10', '2011-04-11', '2011-04-12',\n",
       "               '2011-04-13', '2011-04-14', '2011-04-15', '2011-04-16',\n",
       "               '2011-04-17', '2011-04-18', '2011-04-19', '2011-04-20'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('4/1/2011',periods = 20)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-04-29', '2011-05-31', '2011-06-30', '2011-07-29',\n",
       "               '2011-08-31', '2011-09-30', '2011-10-31', '2011-11-30',\n",
       "               '2011-12-30', '2012-01-31', '2012-02-29', '2012-03-30',\n",
       "               '2012-04-30', '2012-05-31', '2012-06-29', '2012-07-31',\n",
       "               '2012-08-31', '2012-09-28', '2012-10-31', '2012-11-30',\n",
       "               '2012-12-31', '2013-01-31', '2013-02-28', '2013-03-29',\n",
       "               '2013-04-30'],\n",
       "              dtype='datetime64[ns]', freq='BM')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('4/1/2011','5/2/2013',freq = 'BM')   ## BM = Business end of Month 每月最后一天"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-04-01 12:00:01', '2011-04-02 12:00:01',\n",
       "               '2011-04-03 12:00:01', '2011-04-04 12:00:01',\n",
       "               '2011-04-05 12:00:01', '2011-04-06 12:00:01',\n",
       "               '2011-04-07 12:00:01', '2011-04-08 12:00:01',\n",
       "               '2011-04-09 12:00:01', '2011-04-10 12:00:01',\n",
       "               '2011-04-11 12:00:01', '2011-04-12 12:00:01',\n",
       "               '2011-04-13 12:00:01', '2011-04-14 12:00:01',\n",
       "               '2011-04-15 12:00:01', '2011-04-16 12:00:01',\n",
       "               '2011-04-17 12:00:01', '2011-04-18 12:00:01',\n",
       "               '2011-04-19 12:00:01', '2011-04-20 12:00:01'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('4/1/2011 12:00:01',periods = 20) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-04-01', '2011-04-02', '2011-04-03', '2011-04-04',\n",
       "               '2011-04-05', '2011-04-06', '2011-04-07', '2011-04-08',\n",
       "               '2011-04-09', '2011-04-10', '2011-04-11', '2011-04-12',\n",
       "               '2011-04-13', '2011-04-14', '2011-04-15', '2011-04-16',\n",
       "               '2011-04-17', '2011-04-18', '2011-04-19', '2011-04-20'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('4/1/2011 12:00:01',periods = 20, normalize=True)  ## 全部规范到0点了"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**freq频率和时间偏移**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.tseries.offsets import Hour, Minute, Day, MonthEnd\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**需要参考时间序列的基础频率**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-01-01 00:00:00', '2011-01-11 02:30:00',\n",
       "               '2011-01-21 05:00:00', '2011-01-31 07:30:00',\n",
       "               '2011-02-10 10:00:00', '2011-02-20 12:30:00',\n",
       "               '2011-03-02 15:00:00', '2011-03-12 17:30:00',\n",
       "               '2011-03-22 20:00:00', '2011-04-01 22:30:00',\n",
       "               ...\n",
       "               '2016-09-22 13:30:00', '2016-10-02 16:00:00',\n",
       "               '2016-10-12 18:30:00', '2016-10-22 21:00:00',\n",
       "               '2016-11-01 23:30:00', '2016-11-12 02:00:00',\n",
       "               '2016-11-22 04:30:00', '2016-12-02 07:00:00',\n",
       "               '2016-12-12 09:30:00', '2016-12-22 12:00:00'],\n",
       "              dtype='datetime64[ns]', length=217, freq='14550T')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('1/1/2011','1/1/2017',freq = '10d2h30min') ## 以10天2小时30分钟作为一个周期"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-29',\n",
       "               '2011-05-31', '2011-06-30', '2011-07-29', '2011-08-31',\n",
       "               '2011-09-30', '2011-10-31', '2011-11-30', '2011-12-30',\n",
       "               '2012-01-31', '2012-02-29', '2012-03-30', '2012-04-30',\n",
       "               '2012-05-31', '2012-06-29', '2012-07-31', '2012-08-31',\n",
       "               '2012-09-28', '2012-10-31', '2012-11-30', '2012-12-31',\n",
       "               '2013-01-31', '2013-02-28', '2013-03-29', '2013-04-30',\n",
       "               '2013-05-31', '2013-06-28', '2013-07-31', '2013-08-30',\n",
       "               '2013-09-30', '2013-10-31', '2013-11-29', '2013-12-31',\n",
       "               '2014-01-31', '2014-02-28', '2014-03-31', '2014-04-30',\n",
       "               '2014-05-30', '2014-06-30', '2014-07-31', '2014-08-29',\n",
       "               '2014-09-30', '2014-10-31', '2014-11-28', '2014-12-31',\n",
       "               '2015-01-30', '2015-02-27', '2015-03-31', '2015-04-30',\n",
       "               '2015-05-29', '2015-06-30', '2015-07-31', '2015-08-31',\n",
       "               '2015-09-30', '2015-10-30', '2015-11-30', '2015-12-31',\n",
       "               '2016-01-29', '2016-02-29', '2016-03-31', '2016-04-29',\n",
       "               '2016-05-31', '2016-06-30', '2016-07-29', '2016-08-31',\n",
       "               '2016-09-30', '2016-10-31', '2016-11-30', '2016-12-30'],\n",
       "              dtype='datetime64[ns]', freq='BM')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('1/1/2011','1/1/2017',freq = 'BM') ## BM是每个月最后一个工作日，M是每个月最后一天"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**WOM 日期 （Week Of Month)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-31    0.574018\n",
       "2018-02-28    1.099188\n",
       "2018-03-31   -0.100453\n",
       "2018-04-30   -1.050006\n",
       "2018-05-31   -1.312533\n",
       "2018-06-30   -0.545655\n",
       "2018-07-31    0.123292\n",
       "2018-08-31    0.241423\n",
       "2018-09-30    1.039583\n",
       "2018-10-31    2.109472\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(10),index = pd.date_range('2018/01/01',periods=10, freq = 'M'))\n",
    "ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**shift保持索引不变，数据移动（小心，整个datafram会被改变)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-31          NaN\n",
       "2018-02-28          NaN\n",
       "2018-03-31    -0.175000\n",
       "2018-04-30    -0.955256\n",
       "2018-05-31    13.066076\n",
       "2018-06-30     0.519668\n",
       "2018-07-31    -0.093934\n",
       "2018-08-31    -0.442446\n",
       "2018-09-30     8.431879\n",
       "2018-10-31     8.737673\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts / ts.shift(2) ## 移动的是数据，时间序列没改变"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-03-31    0.574018\n",
       "2018-04-30    1.099188\n",
       "2018-05-31   -0.100453\n",
       "2018-06-30   -1.050006\n",
       "2018-07-31   -1.312533\n",
       "2018-08-31   -0.545655\n",
       "2018-09-30    0.123292\n",
       "2018-10-31    0.241423\n",
       "2018-11-30    1.039583\n",
       "2018-12-31    2.109472\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(2,freq ='M') ## 移动的时间序列，数据没有少"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**rollfoware,rollback锚点偏移量**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<10 * MonthEnds>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offset = MonthEnd(10)\n",
    "offset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2018, 9, 21, 6, 15, 51, 115688)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2018-09-30 06:15:51.115688')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offset.rollforward(now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-01-01    0.325941\n",
       "2017-01-05    1.539194\n",
       "2017-01-09    0.612828\n",
       "2017-01-13   -0.847147\n",
       "2017-01-17   -0.585982\n",
       "2017-01-21   -0.348241\n",
       "2017-01-25   -0.010829\n",
       "2017-01-29   -0.522178\n",
       "2017-02-02    1.117981\n",
       "2017-02-06   -0.916257\n",
       "2017-02-10    0.338294\n",
       "2017-02-14    1.249560\n",
       "2017-02-18   -0.074006\n",
       "2017-02-22   -0.419413\n",
       "2017-02-26   -1.542494\n",
       "2017-03-02   -0.345037\n",
       "2017-03-06   -0.622881\n",
       "2017-03-10    0.775680\n",
       "2017-03-14    0.275692\n",
       "2017-03-18    1.557854\n",
       "2017-03-22   -0.146654\n",
       "2017-03-26   -0.621211\n",
       "2017-03-30   -0.023271\n",
       "2017-04-03   -0.789121\n",
       "2017-04-07    0.859332\n",
       "2017-04-11    0.805846\n",
       "2017-04-15   -0.555458\n",
       "2017-04-19    0.199899\n",
       "2017-04-23   -0.940088\n",
       "2017-04-27    0.797756\n",
       "                ...   \n",
       "2017-10-08    0.277662\n",
       "2017-10-12   -0.012389\n",
       "2017-10-16    0.715409\n",
       "2017-10-20   -0.838917\n",
       "2017-10-24    0.622157\n",
       "2017-10-28   -0.227817\n",
       "2017-11-01    0.283799\n",
       "2017-11-05    1.806495\n",
       "2017-11-09   -0.100723\n",
       "2017-11-13    0.994841\n",
       "2017-11-17   -2.379259\n",
       "2017-11-21   -0.402158\n",
       "2017-11-25    0.074915\n",
       "2017-11-29    0.448750\n",
       "2017-12-03    0.850032\n",
       "2017-12-07    1.210926\n",
       "2017-12-11    0.534084\n",
       "2017-12-15    0.588339\n",
       "2017-12-19    1.696954\n",
       "2017-12-23   -1.987578\n",
       "2017-12-27   -0.669377\n",
       "2017-12-31    0.035077\n",
       "2018-01-04   -1.270366\n",
       "2018-01-08   -0.952905\n",
       "2018-01-12    0.492842\n",
       "2018-01-16   -0.224831\n",
       "2018-01-20   -0.307869\n",
       "2018-01-24   -0.242113\n",
       "2018-01-28   -0.160409\n",
       "2018-02-01   -0.019357\n",
       "Freq: 4D, Length: 100, dtype: float64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(100),index=pd.date_range(start = '1/1/2017',periods=100,freq = \"4d\"))\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-01-31   -0.183357\n",
       "2017-02-28    2.316592\n",
       "2017-03-31   -0.175083\n",
       "2017-04-30   -1.269801\n",
       "2017-05-31   -1.532770\n",
       "2017-06-30   -3.463608\n",
       "2017-07-31    1.397774\n",
       "2017-08-31    3.474662\n",
       "2017-09-30   -2.358470\n",
       "2017-10-31    3.530173\n",
       "2017-11-30    0.915685\n",
       "2017-12-31    3.490710\n",
       "2018-01-31    4.389871\n",
       "2018-02-28    0.412759\n",
       "dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.groupby(offset.rollforward).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-01-31   -0.022920\n",
       "2017-02-28    0.330942\n",
       "2017-03-31   -0.021885\n",
       "2017-04-30   -0.181400\n",
       "2017-05-31   -0.191596\n",
       "2017-06-30   -0.432951\n",
       "2017-07-31    0.199682\n",
       "2017-08-31    0.434333\n",
       "2017-09-30   -0.294809\n",
       "2017-10-31    0.504310\n",
       "2017-11-30    0.114461\n",
       "2017-12-31    0.436339\n",
       "2018-01-31    0.627124\n",
       "2018-02-28    0.412759\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('M').mean() ## 可以使用resample，写法更简单"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**时区调整， pytz库**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pytz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['US/Central', 'US/Eastern', 'US/Hawaii', 'US/Mountain', 'US/Pacific', 'UTC']"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pytz.common_timezones[-6:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'US/Hawaii'"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz = pytz.timezone('US/Hawaii')\n",
    "str(tz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**pandas画图**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-01-01    0.541116\n",
       "2017-01-05   -0.965503\n",
       "2017-01-09    0.230030\n",
       "2017-01-13    0.587222\n",
       "2017-01-17    1.099498\n",
       "2017-01-21   -1.186084\n",
       "2017-01-25   -0.447030\n",
       "2017-01-29   -0.042606\n",
       "2017-02-02    0.573677\n",
       "2017-02-06    0.308026\n",
       "2017-02-10    1.468908\n",
       "2017-02-14   -0.342624\n",
       "2017-02-18   -0.520637\n",
       "2017-02-22    1.152395\n",
       "2017-02-26   -0.323153\n",
       "2017-03-02    0.366921\n",
       "2017-03-06   -0.767252\n",
       "2017-03-10    0.431629\n",
       "2017-03-14   -1.436694\n",
       "2017-03-18    0.399386\n",
       "2017-03-22   -1.400274\n",
       "2017-03-26    1.521296\n",
       "2017-03-30    0.709904\n",
       "2017-04-03   -2.392442\n",
       "2017-04-07    0.006390\n",
       "2017-04-11   -0.450353\n",
       "2017-04-15    0.723505\n",
       "2017-04-19    0.741029\n",
       "2017-04-23    0.443791\n",
       "2017-04-27   -0.341721\n",
       "                ...   \n",
       "2017-10-08    0.674794\n",
       "2017-10-12    0.734812\n",
       "2017-10-16   -0.545369\n",
       "2017-10-20    0.953113\n",
       "2017-10-24    0.407173\n",
       "2017-10-28   -1.442863\n",
       "2017-11-01    0.099536\n",
       "2017-11-05    0.567935\n",
       "2017-11-09    1.630107\n",
       "2017-11-13   -1.387016\n",
       "2017-11-17    0.103657\n",
       "2017-11-21    0.310968\n",
       "2017-11-25    0.981352\n",
       "2017-11-29   -1.390856\n",
       "2017-12-03   -0.940390\n",
       "2017-12-07   -0.233682\n",
       "2017-12-11    1.361492\n",
       "2017-12-15   -0.455908\n",
       "2017-12-19    0.851447\n",
       "2017-12-23    1.115484\n",
       "2017-12-27    1.866328\n",
       "2017-12-31   -0.074061\n",
       "2018-01-04   -1.298285\n",
       "2018-01-08    1.820625\n",
       "2018-01-12    1.441607\n",
       "2018-01-16   -2.164413\n",
       "2018-01-20   -0.648974\n",
       "2018-01-24    2.604232\n",
       "2018-01-28    2.635080\n",
       "2018-02-01    0.412759\n",
       "Freq: 4D, Length: 100, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cacdc9f8d0>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cacf143a20>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.loc['2017'].plot() ## 按照时间序列画图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cacf2070f0>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.loc['2017/1/1':'2017/10/1'].plot() ## 时间段画图，中间是:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-03-31   -0.023271\n",
       "2017-06-30   -0.331864\n",
       "2017-09-30    0.696199\n",
       "2017-12-31    0.035077\n",
       "Freq: Q-DEC, dtype: float64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_q = ts['2017'].resample('Q-DEC').ffill()\n",
    "ts_q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cacf302dd8>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts_q.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
